An increase in world population along with a significant aging portion is forcing rapid rises in healthcare costs. The healthcare system is going through a transformation in which continuous monitoring of inhabitants is possible even without hospitalization. The advancement of sensing technologies, embedded systems, wireless communication technologies, nano technologies, and miniaturization makes it possible to develop smart systems to monitor activities of human beings continuously. Wearable sensors detect abnormal and/or unforeseen situations by monitoring physiological parameters along with other symptoms. Therefore, necessary help can be provided in times of dire need. This paper reviews the latest reported systems on activity monitoring of humans based on wearable sensors and issues to be addressed to tackle the challenges.

Wearable Sensors for Human Activity Monitoring: A Review

A highly adaptive multi-sensor SoC comprising four integrated on-chip sensors and a smart wireless acquisition system is realized in standard CMOS process for the first time. To intelligently process different types (C, R, I, and V) of sensor signals, a linear (R-square is 0.999) and reconfigurable sensor readout is proposed based on switched-capacitor circuit technology. In addition, a dual-input energy harvesting interface with conversion efficiency of 73% is also integrated to pick up light energy and RF power, which potentiates long-term use without battery replacement. The entire SoC occupies die area of 11.25 mm 2 and consumes only 942.9 μW. Experimental results show that four physiological parameters (temperature, glucose and protein concentration, and pH value) can be simultaneously monitored using this chip. This system can be seen as a universal sensor platform. Different types of sensors can be easily integrated into it for convenient use, which dramatically reduces time consuming of building a new sensor system.

A Self-Powered CMOS Reconfigurable Multi-Sensor SoC for Biomedical Applications

pdf/unsupervised-heart-rate-estimation-in-wearables-with-liquid-2fss5s9pr0.pdf

Unsupervised heart-rate estimation in wearables with Liquid states and a probabilistic readout.

A power efficient reconfigurable output-capacitor-less (OCL) low-drop-out (LDO) voltage regulator for low-power analog sensing front-end is proposed in this paper. This LDO consists of a floating-gate n MOS pass transistor, an adaptively biased error amplifier, and capacitive circuits for voltage reference generation and for feedback sensing. The error amplifier adopts a class-AB input differential pair and an adaptively biased regulated cascode topology to improve transient response under the stringent constraint of low quiescent current consumption. The reference voltage is implemented by programming charges on capacitors without employing a bandgap circuit. A prototype chip is designed and fabricated in a 0.35 $\mu\text{m}$ CMOS process to demonstrate the reconfigurability and to validate the performance. The output voltage can be programmed in continuum in the range of 1.2 V to 2.5 V with measured temperature coefficients less than 45 $\text{ppm}/^{\circ}\text{C}$ . The maximum load current is designed to be 1 mA with output voltage drop less than 0.1%. With programmable quiescent current levels less than 1 $\mu\text{A}$ , the current efficiency is higher than 99.9%. From measurements, the line regulation is 0.17 mV/V or $-$ 75 dB. The designed OCL LDO remains stable with maximum output load capacitance up to 1 nF under the zero load condition.

A Power-Efficient Reconfigurable Output-Capacitor-Less Low-Drop-Out Regulator for Low-Power Analog Sensing Front-End

A tutorial on signal energy and its applications

An acoustic object detector for detecting presence of an acoustic signal is provided. The acoustic object detector includes a number of bandpass filters. Each bandpass filter is configured to convert an input signal into an analog signal within a frequency band. The acoustic object detector also includes a number of spike generating circuits each coupled to the respective bandpass filter. Each spike generating circuit is configured to generate a series of spike signals based upon an adaptive threshold for the analog signal. The acoustic object detection further includes a decision circuit configured to generate a digital signal at a time-frequency point from the series of spike signals.

Acoustic sensor with an acoustic object detector for reducing power consumption in front-end circuit

In this paper, we present a microphone preamplifier that adapts its power consumption according to the input signal's instantaneous bandwidth. The preamplifier's dynamic range and gain keep reasonably constant, regardless of the power consumption. The preamplifier was fabricated with a 0.5-µm CMOS process. The measurement results show it has over 79.5-dB dynamic range with 53.4% power saving compared to its non-adaptive counterpart.

An adaptive microphone preamplifier for low power applications

This paper discusses the design challenges that must be met by an audio sensor interface for use in a wearable cough monitoring device. In particular, the paper considers the issues of privacy, power consumption, reliable data collection, and patient identification. A nonlinear, reconfigurable architecture for the audio sensor interface is proposed to address some of these challenges. The new architecture is validated with simulation and measurement results.

Towards a smart sensor interface for wearable cough monitoring

In speech processing applications, the instantaneous bandwidth of speech can be used to adaptively control the performance of an audio sensor's analog front end. Extracting the instantaneous bandwidth of speech depends on the detection of speech edges in the time---frequency plane. In this paper, we propose a spike encoding circuit for real-time and low-power speech edge detection. The circuit can directly encode the signal's envelope information--an important feature to identify the speech edge--by temporal spike density without additional envelope extraction. Furthermore, the spike encoding circuit automatically adapts its resolution to the amplitude of the input signal, which improves the encoding resolution for small signal without increasing the power consumption. We use the nonlinear dynamical approach to design this circuit and analyze its stability. We also develop a linearized model for this circuit to provide the design intuition and to explain its adaptive resolution. Fabricated in 0.5-μm CMOS process, the spike encoding circuit consumes 0.3-μW power and the experimental results are presented.

An energy-efficient spike encoding circuit for speech edge detection

Intelligent audio sensors that are continuously recording and analyzing sounds are a critical component of many emerging and future embedded applications. In these applications, the power budget is very tight, of which the analog front end consumes a major proportion. An efficient analog front end should adapt its power consumption to the instantaneous bandwidth of the audio signal of interest, instead of constantly consuming a fixed amount of power that assumes a fixed signal bandwidth. In this paper, we introduce a novel algorithm for identifying the edges of speech in the time-frequency domain, which is used to detect the instantaneous bandwidth of speech. A circuit implementation of our algorithm consumes 42.4µW of power and can extract the instantaneous bandwidth of a signal within an accuracy of 1% even in SNR conditions as low as 10 dB.

Efficient speech edge detection for mobile health applications

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